Currently, cryptographic algorithms are widely applied to communications systems to guarantee data security. For instance, in an emerging automotive environment where connectivity is a core part of autonomous and connected cars, it is essential to guarantee secure communications both inside and outside the vehicle. The AES algorithm has been widely applied to protect communications in onboard networks and outside the vehicle. Hardware implementations use techniques such as iterative, parallel, unrolled, and pipeline architectures. Nevertheless, the use of AES does not guarantee secure communication, because previous works have proved that implementations of secret key cryptosystems, such as AES, in hardware are sensitive to differential fault analysis. Moreover, it has been demonstrated that even a single fault during encryption or decryption could cause a large number of errors in encrypted or decrypted data. Although techniques such as iterative and parallel architectures have been explored for fault detection to protect AES encryption and decryption, it is necessary to explore other techniques such as pipelining. Furthermore, balancing a high throughput, reducing low power consumption, and using fewer hardware resources in the pipeline design are great challenges, and they are more difficult when considering fault detection and correction. In this research, we propose a novel hybrid pipeline hardware architecture focusing on error and fault detection for the AES cryptographic algorithm. The architecture is hybrid because it combines hardware and time redundancy through a pipeline structure, analyzing and balancing the critical path and distributing the processing elements within each stage. The main contribution is to present a pipeline structure for ciphering five times on the same data blocks, implementing a voting module to verify when an error occurs or when output has correct cipher data, optimizing the process, and using a decision tree to reduce the complexity of all combinations required for evaluating. The architecture is analyzed and implemented on several FPGA technologies, and it reports a throughput of 0.479 Gbps and an efficiency of 0.336 Mbps/LUT when a Virtex-7 is used.
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http://dx.doi.org/10.3390/s21165655 | DOI Listing |
Sci Rep
January 2025
School of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang University, Ürümqi, 830017, China.
The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguarding the lives of coal mine workers. Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults.
View Article and Find Full Text PDFSci Data
January 2025
Sustainability/Net-Zero Office, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
This paper presents an open-source dataset intended to enhance the analysis and optimization of photovoltaic (PV) power generation in urban environments, serving as a valuable resource for various applications in solar energy research and development. The dataset comprises measured PV power generation data and corresponding on-site weather data gathered from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals at the inverter-level.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, South China University of Technology, Guangzhou, 510640, China; Institute for Super Robotics (Huangpu), Guangzhou, 510555, China; Nanchang University, Nanchang, 330031, China; College of Computer Science and Engineering, Jishou University, Jishou, 416000, China; Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, 510335, China; School of Electronical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China; School of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China; Institute of Artificial Intelligence and Automation, Guangdong University of Petrochemical Technology, Maoming, 525000, China. Electronic address:
To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is proposed to realize multi-classification of transformer faults based on dissolved gas data. Then, a swarm budorcas taxicolor optimization (SBTO) algorithm is proposed to iteratively search the optimal model parameters during MSV model training, so as to obtain the most effective transformer fault diagnosis model.
View Article and Find Full Text PDFEnviron Res
January 2025
Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; Shandong Huatai Paper Co. Ltd., Dongying 257335, China. Electronic address:
Wastewater treatment systems are essential for sustainable water resource management but face challenges such as equipment and sensor malfunctions, fluctuating influent conditions, and operational disturbances that compromise process stability and detection accuracy. To address these challenges, this paper systematically reviews data-driven fault detection and diagnosis (FDD) methods applied in wastewater treatment systems from 2014 to 2024, focusing on their applications, advancements, and limitations. Main contributions include an overview of key treatment processes, a detailed evaluation of fault types (process and sensor faults), advancements in multivariate statistical methods, machine learning (ML), and hybrid FDD techniques, as well as their effectiveness in anomaly detection, managing complex data distributions, and enabling real-time monitoring.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science, South China Normal University, Guangzhou 510555, China.
Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze anomalous behavior in complex systems, which is particularly important in fields such as financial monitoring, industrial equipment fault detection, and cybersecurity. MTSAD requires simultaneously analyze temporal dependencies and inter-variable relationships have prompted researchers to develop specialized deep learning models to detect anomalous patterns. In this paper, we conducted a structured and comprehensive overview of the latest techniques in deep learning for multivariate time series anomaly detection methods.
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